Incremental Learning(IL) is also referred to as: Continual Learning(CL), Life Long Learning(LLL), Online Learning(OL), Never Ending Learning(NEL).

Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.¹⁾

Catastrophic Forgetting(CF): Learning multiple tasks in sequence, however, remains a substantial challenge for deep learning. When trained on a new task, standard neural networks forget most of the information related to previously learned tasks, a phenomenon referred to as “catastrophic forgetting”²⁾.

There are 3 scenarios on Incremental Learning³⁾, including:

• Task Incremental Learning
• Domain Incremental Learning
• Class Incremental Learning

All methods of incremental learning can be concluded into 4 categories⁴⁾.

• Task specific components(sub-network per task): XDG(Context-dependent Gating)
• regularized optimization(differently regularizing parameters): EWC(Elastic Weight Consolidation), SI(Synaptic Intelligence)
• Modifying Training Data(pseudo-data, generate samples): LwF(Learning without Forgetting), DGR(Deep Generative Replay)
• Using Exemplars(store data from previous tasks): iCaRL

Catastrophic Forgetting by Hung-yi Lee

Mitigating Catastrophic Forgetting by Hung-yi Lee

Awesome Incremental Learning Papers

Incremental Learning Papers